计算生物与医学生态学（马占山）知识库

The intra-tumor microbiome can impact the tumor's behavior by influencing its growth, inflammatory reactions, evasion of the immune system, genomic instability, and drug resistance. Altering this microbiota to improve the response to cancer treatment could offer fresh perspectives on cancer therapy. The very first step in intervening in the microbiome is to gain a deep understanding of how microbial diversity varies spatially and temporally between tissues or among individuals. Such changes can be investigated with the so-termed diversity-area relationship (DAR) modeling (Ma. 2018. Ecology and Evolution, 8(20), 10023-10038). This included examining the DAR profiles, Pairwise Diversity Overlap (PDO) profiles, Maximum Accumulated Diversity (MAD) profiles, and the ratio of local to global accumulated diversity (LGD) profiles. This study applies the DAR approach to reanalyze five lung tissue microbiome datasets to shed light on how the microbial diversity changes across tissue types and across individual patients. We characterized the diversity scaling of human lung cancer microbiota from aspects such as microbial community diversity, variation rates of diversity, similarity, and microbial community proportions. The generated results indicate that there are no statistically significant differences in the DAR scaling parameters across different tissue types, suggesting that the diversity scaling of microbial communities in normal and tumor tissues across individuals seems to be invariant. The invariance is simply a reflection of the resilience of lung tissue microbiome against disturbance such as lung cancer, and thus further studies at the level of microbial species to better understand their relationship with cancer are critical.